Figure 3.

Cross-mapping correction strategy. For each short RNA that can be aligned to multiple genome regions with an equal number of errors, our strategy to correct for cross-mapping assigns weights to each candidate mapping location based on the local expression level as well as the alignment errors. The latter is based on the error profile describing the probability of an alignment error as a function of the position along the alignment. Both the local expression level and the error profile are calculated from the mapped RNAs themselves using an expectation-maximization algorithm. First, we assign equal weights to all candidate mapping locations. We then calculate the error profile and the expression level of each genome location from the complete set of mapped RNA sequences. This allows us then to recalculate the error profile and the expression levels. This process is iterated until convergence.